一句话：提出基于 Issue 工单 NLP 的语言无关代码债务检测方法，并用 git 挖掘计算债务存续时间与经济成本。
论文针对现有代码债务/代码坏味检测多依赖静态代码分析且通常局限于单一编程语言的问题，提出利用 Issue/Ticket 工单进行检测。方法对工单文本应用自然语言处理来识别代码债务，并借助 git 挖掘计算代码债务在软件中存续的平均时间。该工作强调多语言开发栈下的适用性，并引入经济成本视角；摘要未报告具体实验数据或评估结果。
Despite the fact that there are numerous classifications of technical debt based on various criteria, Code Debt or code smells is a category that appears in the majority of current research. One of the primary causes of code debt is the urgency to deliver software quickly, as well as bad coding practices. Among many approaches, static code analysis has received the most attention in studies to detect code-smell/code debt. However, most of them examine the same programming language, although today’s software company utilizes many development stacks with various languages and tools. This problem can be resolved by detecting code debt with Issue/Ticket cards. This paper presents a method for detecting code debt leveraging natural language processing on issue tickets. It also proposes a method for calculating the average amount of time that a code debt was present in the software. This method is implemented utilizing git mining.
DOI 原文 ·
@inproceedings{paperbot6586,
title = {Using Version Control and Issue Tickets to detect Code Debt and Economical Cost},
author = {Abdullah Al Maruf and Noah Lambaria and Amr S. Abdelfattah and Tomas Cerny},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678532}
}